Connectionist model based local optimization algorithm for large-scale water pollution monitoring data fusion systems
基于连接模型的局部优化算法在水域污染监测数据融合系统中的应用

Aiming at the difficulties existing in large-scale water pollution monitoring systems, a connectionist model based local optimization algorithm and its application are discussed in this paper. With just the excitatory connections the connectionist model drastically reduced the storage for links and the fanouts of the nodes. Based on the competitive activation mechanism, the local optimization algorithm and its improvement-partial resettling algorithm, realize the dynamically changing functional relationships between disorders and appropriate multiple-winners-take-all behavior. As an illustrative example, the connectionist model is introduced to the water pollution monitoring data fusion system. Computer simulation results show that the local optimization algorithm and the partial resettling algorithm greatly save the computation time, as well as ensure that the most probable disorders can be founded.